Photoplethysmography is a non-invasive measurement of the blood flow at the surface of the skin of a human by using two-wavelength lights, such as red (R) and infrared (IR) lights, to generate photoplethysmographic (“PPG”) signals. Two common uses of the PPG signals are calculations of the arterial oxygen saturation and heart rate. Several applications that require various analyses of the PPG signals include amplitude, rhythm, peripheral pulse, respiratory variability and tissue perfusion. For example, increased and decreased signal amplitude can indicate signs of vasodilation and vasoconstriction, respectively. The amplitude is directly proportional to the vascular distensibility. PPG signals are also useful for the detection and diagnosis of cardiac arrhythmias. Further, a PPG signal is known to be sensitive to pulsatile blood flow and captures the peripheral pulses. The pressure at which the pulse is captured highly corresponds to the systolic blood pressure. The respiratory rate can be determined by a set of PPG signals. Noninvasive continuous tissue perfusion and peripheral blood flow detection is another potential advantage of the PPG signal. All the above-mentioned applications require a clean and enhanced signal for feature extraction, analysis, and monitoring. Therefore, the quality of the PPG signal is critical for wearable PPG devices and systems.
For example, in wearable and implantable devices/applications, biometric signals need to be monitored during daily activities where motion is always present. Motion artifact is the most problematic source of noise which deteriorates signal integrity and can, in the worst case, corrupt the PPG signal to such an extent that the signal is rendered clinically unusable. Examples of motions of the patient in a real world clinical setting include movement during transport, rubbing, waving, seizures, and kicking in neonates and infants. As a result, inaccurate readings and interpretations of the PPG signal due to motion artifact increases the workload of a caregiver of the patient which leads to an increased cost of care and inefficiency of patient's treatment. Therefore, there is a need in the art for an effective solution for wearable and mobile PPG biosensors to enhance the signal quality in the presence of motion artifact.
The prior art has attempted to solve these problems with limited success. For example, one commonly used method to reduce the effect of motion artifact is adaptive noise cancellation using accelerometers as a noise reference signal. A two-dimensional active noise cancellation uses the directional accelerometer data for a finger PPG sensor. Another method adds a reflectance PPG sensor as the reference signal to reduce the effect of motion artifact. However, the reflectance PPG sensor is itself susceptible to motion artifact. The main drawback of all these methods is the cost of extra hardware for the generation of the noise reference signal. Further, using accelerometer data is computationally intensive and reflects motion as opposed to motion-induced noise. More precisely, no direct or high correlation between acceleration data from an accelerometer and motion artifact in PPG signal has been found. This method assumes that the original PPG signal has only power at certain frequencies with the remaining power assumed to be noise and then uses Fast Fourier Transform (FFT), Singular Value Decomposition and Independent Component Analysis to generate three synthetic reference noise signals. The method switches between the three reference noise signals by quantifying the randomness of each signal using skewness and kurtosis. These assumptions on motion artifact do not correlate with different real-world sources of noise. Moreover, the highest randomness does not necessarily mean the highest correlation with the true motion artifact in the PPG signal.
In another example, Masimo Corporation has introduced Discrete Saturation Transform (DST) to find pulse oximeter oxygen saturation in the presence of motion in portable devices. Typically, the DST method includes of a reference signal generator, an adaptive filter and a peak finder to find the most likely SpO2 value based on the incoming signals. In this approach, the reference signal generator produces reference signals for all possible SpO2 values. For each reference signal, the adaptive filter produces an output signal. Energy of each output signal is computed and plotted versus corresponding SpO2 values. The right-most peak of the power plot (the largest saturation value) is nominally considered as oxygen saturation of arterial blood flow. However, this approach does not remove the motion artifact (e.g. due to tissue effect). Consequently, the effect of motion artifact is transformed to the output power plot in DST. More specifically, in presence of motion artifact, new peaks will be present on the output plot and the peak finder fails to find the peak corresponding to accurate SpO2. Alternatively, the peak corresponding to SpO2 is concealed due to high motion noise power causing the peak search to fail for that time window.
Therefore, there is a need in the art for an effective solution for wearable and mobile PPG biosensors to enhance the signal quality in the presence of motion artifact.